Combining Contrastive and Supervised Learning for Video Super-Resolution Detection

• URL: http://arxiv.org/abs/2205.10406v1
• Date: Fri, 20 May 2022 18:58:13 GMT
• Title: Combining Contrastive and Supervised Learning for Video Super-Resolution Detection
• Authors: Viacheslav Meshchaninov, Ivan Molodetskikh, Dmitriy Vatolin
• Abstract summary: We propose a new upscaled-resolution-detection method based on learning of visual representations using contrastive and cross-entropy losses. Our method effectively detects upscaling even in compressed videos and outperforms the state-of-the-art alternatives.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Upscaled video detection is a helpful tool in multimedia forensics, but it is a challenging task that involves various upscaling and compression algorithms. There are many resolution-enhancement methods, including interpolation and deep-learning-based super-resolution, and they leave unique traces. In this work, we propose a new upscaled-resolution-detection method based on learning of visual representations using contrastive and cross-entropy losses. To explain how the method detects videos, we systematically review the major components of our framework - in particular, we show that most data-augmentation approaches hinder the learning of the method. Through extensive experiments on various datasets, we demonstrate that our method effectively detects upscaling even in compressed videos and outperforms the state-of-the-art alternatives. The code and models are publicly available at https://github.com/msu-video-group/SRDM

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